scholarly journals An Effective Encoding Method Based on Local Information for 3D Point Cloud Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39369-39377
Author(s):  
Yanan Song ◽  
Liang Gao ◽  
Xinyu Li ◽  
Quan-Ke Pan
Keyword(s):  
Point Cloud ◽  
Local Information ◽  
3D Point Cloud ◽  
Cloud Classification ◽  
Encoding Method ◽  
Point Cloud Classification

scholarly journals Dissimilarity to Class Medoids as Features for 3D Point Cloud Classification

2021 ◽  
pp. 573-581
Author(s):  
Sylvain Chabanet ◽  
Valentin Chazelle ◽  
Philippe Thomas ◽  
Hind Bril El-Haouzi
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Cloud Classification ◽  
Point Cloud Classification

scholarly journals Multi-view attention-convolution pooling network for 3D point cloud classification

2021 ◽  
Author(s):  
Wenju Wang ◽  
Tao Wang ◽  
Yu Cai
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Information Loss ◽  
Feature Representation ◽  
3D Point Cloud ◽  
Current Output ◽  
3D Point Clouds ◽  
Cloud Classification ◽  
Feature Information ◽  
Point Cloud Classification

AbstractClassifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

scholarly journals GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

2022 ◽  
Vol 12 (1) ◽  
pp. 483
Author(s):  
Long Hoang ◽  
Suk-Hwan Lee ◽  
Eung-Joo Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Autonomous Vehicles ◽  
Point Cloud ◽  
High Performance ◽  
3D Point Cloud ◽  
Real World Data ◽  
Color Representation ◽  
Princeton University ◽  
Cloud Classification ◽  
Sensing Technology ◽  
Point Cloud Classification

Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous vehicles, robotics, and virtual and augmented reality (VR/AR). The 3D point cloud classification is now a hot research topic with the evolution of LiDAR technology. This research aims to provide a high performance and compatible real-world data method for 3D point cloud classification. More specifically, we introduce a novel framework for 3D point cloud classification, namely, GSV-NET, which uses Gaussian Supervector and enhancing region representation. GSV-NET extracts and combines both global and regional features of the 3D point cloud to further enhance the information of the point cloud features for the 3D point cloud classification. Firstly, we input the Gaussian Supervector description into a 3D wide-inception convolution neural network (CNN) structure to define the global feature. Secondly, we convert the regions of the 3D point cloud into color representation and capture region features with a 2D wide-inception network. These extracted features are inputs of a 1D CNN architecture. We evaluate the proposed framework on the point cloud dataset: ModelNet and the LiDAR dataset: Sydney. The ModelNet dataset was developed by Princeton University (New Jersey, United States), while the Sydney dataset was created by the University of Sydney (Sydney, Australia). Based on our numerical results, our framework achieves more accuracy than the state-of-the-art approaches.

scholarly journals SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK

2017 ◽  
Vol IV-1/W1 ◽  
pp. 91-98 ◽  
Author(s):  
T. Hackel ◽  
N. Savinov ◽  
L. Ladicky ◽  
J. D. Wegner ◽  
K. Schindler ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Full Potential ◽  
3D Point Cloud ◽  
Learning Methods ◽  
Data Set ◽  
Cloud Classification ◽  
Wide Range ◽  
Point Cloud Classification

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

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